Two-dimensional infrared photo-detectors require a normalization process to remove random offsets and gain differentials between the individual pixels in order that the image presented to the user is free of detector noise. The “level offsets” are the pixel outputs when there is no incident flux striking the detector pixels. The “gain” is the relative response of output signal versus input signal. For an optical detector system, a typical detector output is a voltage or current level, and the input signal is the photon flux from the scene. The process of normalizing both the gain and level offset detector pixel-to-pixel variation is known as non-uniformity correction, sometimes abbreviated as “NUC”. This process is particularly important for detectors made of HgCdTe (mercury-cadmium-telluride) which is notorious for having significant levels of pixel non-uniformity despite the best manufacturing processes. A typical mathematical relation for the NUC process is as follows:(Detector Pixel Output Signal)=(Gain)*(Input Signal)+(Offset)This has exactly the same nature as the familiar linear equation,y=mx+b where y is the output, m is the gain slope, x is the input signal, and b is the offset. The NUC process depends on accurately determining the absolute value x for any given output y by means of knowing the precise values of m and b. Detector devices are typically designed to produce a constant gain function, but it is certainly possible that the same process will work with polynomial or non-linear functions. In practice, it may also be found that these parameters are also functions of ambient temperatures, device ageing, and internal electronic settings.
The most direct and accurate prior art method of the NUC process requires that one or more mechanical structures of uniform spectral radiance be inserted in front of the detector array to block out the scene and thus ensure that each pixel is receiving the same incident flux. The pixel values can then be offset accordingly for that given flux level. Advanced systems may further include either two separate blocking structures of different apparent temperatures, or perhaps one structure with a variable temperature to adjust the photon flux output. Then by comparing the responses over two or more flux levels, the pixel gains can also be normalized. Devices have been specifically manufactured for this method. Specifically, a Thermal Reference Source (TRS) device consists of a smooth surface with a temperature stabilized control circuit which feeds back to the system electronics the value of the infrared photon flux by means of Planck's blackbody radiation laws. An example of a commercially available TRS product is the Model ST3821, produced by II-VI Marlow of Dallas, Tex. In order for the sensor to periodically see the TRS, a moving mirror and possibly one or more imaging lenses are also required to properly illuminate the focal plane with the TRS output. All of these materials are relatively expensive and require significant labor costs for installation and verification.
Another suggested NUC process is to vary the bias voltage of the sensor Read-Out Integrated Circuit (ROIC) in order to calculate gain and level coefficients. In this method, a shutter is used to provide uniform irradiance onto the detector array. Then, manipulating the same linear equation but considering the known bias voltage in the gain term, the gain and offset terms are calculated based on the average response of the detectors as the “expected” value. See, X. Chen, Y. Li, C. Di, X. Wang, Y. Cao, “A novel non-uniformity correction method based on ROIC,” In Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80020O, Dec. 8, 2011, incorporated herein by reference. This method, while effective, may reduce the dynamic range of a scene as it averages the signal across the pixels from the uniform shutter. This may not account for other optical effects such as cosine to the fourth loss across the scene. This process also has the significant disadvantage that one or more frames of imagery must be diverted from the scenery to the shutter surface.
Many attempts have been made to perform software-only based NUC processes with varying degrees of success. The most successful software prior-art technique is known as “scene-based NUC”, wherein software analysis of several successive frames of data performs statistical models to predict the pixel gain and offset values. Typically this approach relies on a great deal of laboratory or factory-level calibration of the detector elements for use as look-up tables when used in the field. This approach is highly desirable because it does not require any interruption of the video stream, which is a requirement for tracking or guidance of fast objects such as missiles and jet aircraft. Scene-based techniques certainly do improve the quality of the imagery when the process iterates over several hundred frame analysis sequences, but statistical errors always result from imperfect sampling, limited processing times, and variations in the scene dynamics, and hence this approach has yet to compare with calibration by hardware methods. What is needed is a means of providing gain and offset coefficients to the NUC process which have the accuracy of a hardware calibration, but do not require interrupting the video images of the scenery which the sensor is intended to view.